Non Invasive Sensors Based Driver Drowsiness and Alertness Detection System

Authors

  • Prashrita Kaushal  M.Tech (Electronics Engineering), University of Allahabad, Uttar Pradesh, India

Keywords:

Abstract

Several methods were proposed by different researchers to observe the behavior of the driver. Various factors such as driver behavior, driver state or physiological behavior, vehicle condition, and other external factors are responsible for road accidents. The state of the driver can be categorized as attentive or drowsy. The drowsiness in drivers is due to fatigue, sleepiness, or alcohol intoxication (i.e., being drunk). This paper proposes a system comprising a pulse sensor, accelerometer, and alcohol sensor, etc. that regularly monitors the driver alertness and generates alerts if drowsiness is detected. The algorithm uses driver’s data, sensors data and employs HRV analysis for the determination of driver’s fatigue. Section 3 and 4 of the paper discuss the proposed method and algorithm and section 5 presents the hardware implementation.

References

  1. C. Liu, S. Hosking, and M. Lenne, “Predicting driver drowsiness using vehicle measures: Recent insights and future challenges,” J. Safety Res., vol. 40, no. 4, pp. 239-245, Aug. 2009.
  2. B. F. Wu, Y.-H. Chen, C.-H. Yeh, and Y.-F. Li, “Reasoning-based framework for driving safety monitoring using driving event recognition,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 3, pp. 1231-1241, Sep. 2013.
  3. A. Sahayadhas, K. Sundaraj, and M. Murugappan, “Detecting driver drowsiness based on sensors: A review,” Sensors, vol. 12, pp. 16 937-16 953, 2012.
  4. A. Dasgupta, A. George, S. Happy, and A. Routray, “A vision-based system for monitoring the loss of attention in automotive drivers,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 4, pp. 1825-1838, Dec. 2013.
  5. E. Ohn-Bar and M. Trivedi, “Hand gesture recognition in real time for automotive interfaces: A multimodal vision-based approach and evaluations,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 6, pp. 2368-2377,Dec. 2014.
  6. S. Kar, M. Bhagat, and A. Routary, “EEG signal analysis for the assessment and quantification of drivers fatigue,” Transp. Res. F, Traffic Psychol. Behav., vol. 13, no. 5, pp. 297-306, 2010.
  7. C. Zhang, H. Wang, and R. Fu, “Automated detection of driver fatigue based on entropy and complexity measures,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 1, pp. 168-177, Feb. 2014.
  8. M. Patel, S. Lal, D. Kavanagh, and P. Rossiter, “Applying neural network analysis on heart rate variability data to assess driver fatigue,” Exp. Syst. Appl., vol. 38, pp. 7235-7242, 2011.

International Journal of Scientific Research in Science and Technology

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Published

2019-11-30

Issue

Volume 6, Issue 6, November-December-2019

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prashrita Kaushal, " Non Invasive Sensors Based Driver Drowsiness and Alertness Detection System, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 6, Issue 6, pp.395-400, November-December-2019.